Artificial Intelligence Engineer
- GenAI Integration for SLB Digital Products
- Integrate AI Foundation services (FM Hub, Model Ops, AI Workspace, Agent Workspace, GenAI Infrastructure) into SLB digital products such as Delfi and Lumi.
- Develop reusable integration patterns, APIs, and components that enable scalable, production-ready GenAI capabilities across product teams.
- Deliver hands-on enablement - working demos, reference implementations, and workshops - to accelerate GenAI adoption across domain product teams.
- Domain Foundation Model Integration, Deployment & Benchmarking
- Integrate and deploy domain-specific foundation models into domain workflows and product pipelines, ensuring domain-aligned behavior and performance.
- Collaborate with domain teams to define benchmark datasets, evaluation metrics, and acceptance criteria; establish continuous evaluation frameworks to assess model quality, robustness, and domain relevance.
- Support product teams in embedding benchmarking and evaluation into their development and release processes.
- Optimize deployment workflows for inference efficiency and production readiness.
- Design and build end-to-end agentic solutions - including multi-agent systems, reasoning pipelines, and tool-use orchestration - that address real domain challenges in geoscience and energy.
- Translate domain expertise into agent architectures that combine foundation models, domain data, and simulation tools into compound AI systems.
- Develop agent evaluation frameworks and serve as an internal reference for advanced agentic solution patterns.
- Advanced degree (MS or PhD) in Geophysics, Geoscience, Petroleum Engineering, Computer Science, Electrical Engineering, Applied Math, or equivalent.
- Experience with AI/ML model integration, deployment, and evaluation.
- Proven experience building and deploying agentic AI systems (multi-agent, tool-use, reasoning pipelines) in production.
- Experience with cloud-based AI infrastructure, model operations (Model Ops), and scalable service integration.
- Familiarity with geoscience, subsurface, or energy workflows; a strong understanding of domain problems is highly valued and expected for success in this role.
- Ability to work cross-functionally with product teams, domain experts, and platform engineering groups.
- Strong communication skills, including the ability to deliver technical training and documentation.
- Experience with using foundation models, large language models, domain-specific AI systems, agentic systems.
- Hands-on experience with domain AI applications: seismic interpretation, reservoir simulation.
- Familiarity with subsurface software platforms.
- Experience building benchmarking pipelines, evaluation frameworks, or automated testing systems for AI models.
- Knowledge of agent-based AI architectures and domain agent design.
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